Stress-induced phosphoprotein 1: how does this co-chaperone influence the metastasis steps?

In several cancer types, metastasis is associated with poor prognosis, survival, and quality of life, representing a life risk more significant than the primary tumor itself. Metastasis is a multi-step process that spreads tumor cells from primary sites to surrounding or distant organs, originating secondary tumors. The interconnected steps that drive metastasis depend of several capabilities that enable cells to detach from the primary tumor, acquire motility and migrate through the basal membrane; invade and spread through the vascular system, and finally settle and originate a new tumor. Recently, stress-induced phosphoprotein 1 (STIP1) has emerged as a protein capable of driving tumor cells through these metastasis steps by mediating several biological processes and signaling pathways. This protein is mainly known for its function as a co-chaperone, acting as a scaffold for the interaction of its client heat-shock proteins Hsp70/90 chaperones; however, it is also known that STIP1 can act independently of chaperones to activate downstream phosphorylation pathways. The over-expression of STIP1 has been reported across various cancer types, identifying it as a potential biomarker for predicting patient prognosis and monitoring the progression of metastasis. Here, we present a discussion on how this co-chaperone mediates the initial steps of metastasis (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis), highlighting the biological mechanisms in which STIP1 plays a vital role, also presenting an overview of the current knowledge regarding its clinical relevance.

Targeting EphA2: a promising strategy to overcome chemoresistance and drug resistance in cancer.

Erythropoietin-producing hepatocellular A2 (EphA2) is a vital member of the Eph tyrosine kinase receptor family and has been associated with developmental processes. However, it is often overexpressed in tumors and correlates with cancer progression and worse prognosis due to the activation of its noncanonical signaling pathway. Throughout cancer treatment, the emergence of drug-resistant tumor cells is relatively common. Since the early 2000s, researchers have focused on understanding the role of EphA2 in promoting drug resistance in different types of cancer, as well as finding efficient and secure EphA2 inhibitors. In this review, the current knowledge regarding induced resistance by EphA2 in cancer treatment is summarized, and the types of cancer that lead to the most cancer-related deaths are highlighted. Some EphA2 inhibitors were also investigated. Regardless of whether the cancer treatment has reached a drug-resistance stage in EphA2-overexpressing tumors, once EphA2 is involved in cancer progression and aggressiveness, targeting EphA2 is a promising therapeutic strategy, especially in combination with other target-drugs for synergistic effect. For that reason, monoclonal antibodies against EphA2 and inhibitors of this receptor should be investigated for efficacy and drug toxicity.

Biomarker potential of the LEF1/TCF family members in breast cancer: Bioinformatic investigation on expression and clinical significance.

The LEF1/TCF transcription factor family is related to the development of diverse tissue types, including the mammary tissue, and dysregulation of its expression and function has been described to favor breast tumorigenesis. However, the clinical and biological relevance of this gene family in breast cancer is still poorly understood. Here, we used bioinformatics approaches aiming to reduce this gap. We investigated its expression patterns in molecular and immune breast cancer subtypes; its correlation with immune cell infiltration, and its prognostic values in predicting outcomes. Also, through regulons construction, we determined the genes whose expression is influenced by these transcription factors, and the pathways in which they are involved. We found that LEF1 and TCF3 are over-expressed in breast tumors regarding non-tumor samples, while TCF4 and TCF7 are down-expressed, with the gene's methylation status being associated with its expression dysregulation. All four transcription factors presented significance at the diagnostic and prognostic levels. LEF1, TCF4, and TCF7 presented a significant correlation with immune cell infiltration, being associated with the immune subtypes of less favorable outcomes. Altogether, this research contributes to a more accurate understanding of the expression and clinical and biomarker significance of the LEF1/TCF transcription factors in breast cancer.

High-throughput proteomics of breast cancer subtypes: Biological characterization and multiple candidate biomarker panels to patients' stratification.

BACKGROUND AND AIMS: The actual classification of breast tumors in subtypes represents an attempt to stratify patients into clinically cohesive groups, nevertheless, clinicians still lack reproducible and reliable protein biomarkers for breast cancer subtype discrimination. In this study, we aimed to access the differentially expressed proteins between these tumors and its biological implications, contributing to the subtype's biological and clinical characterization, and with protein panels for subtype discrimination. METHODS: In our study, we applied high-throughput mass spectrometry, bioinformatic, and machine learning approaches to investigate the proteome of different breast cancer subtypes. RESULTS: We identified that each subtype depends on different protein expression patterns to sustain its malignancy, and also alterations in pathways and processes that can be associated with each subtype and its biological and clinical behaviors. Regarding subtype biomarkers, our panels achieved performances with at least 75% of sensibility and 92% of specificity. In the validation cohort, the panels obtained acceptable to outstanding performances (AUC = 0.740 to 1.00). CONCLUSIONS: In general, our results expand the accuracy of breast cancer subtypes' proteomic landscape and improve the understanding of its biological heterogeneity. In addition, we identified potential protein biomarkers for the stratification of breast cancer patients, improving the repertoire of reliable protein biomarkers. SIGNIFICANCE: Breast cancer is the most diagnosed cancer type worldwide and the most lethal cancer in women. As a heterogeneous disease, breast cancer tumors can be classified into four major subtypes, each presenting particular molecular alterations, clinical behaviors, and treatment responses. Thus, a pivotal step in patient management and clinical decisions is accurately classifying breast tumor subtypes. Currently, this classification is made by the immunohistochemical detection of four classical markers (estrogen receptor, progesterone receptor, HER2 receptor, and the Ki-67 index); however, it is known that these markers alone do not fully discriminate the breast tumor subtypes. Also, the poor understanding of the molecular alterations of each subtype leads to a challenging decision-making process regarding treatment choice and prognostic determination. This study, through high-throughput label-free mass-spectrometry data acquisition and downstream bioinformatic analysis, advances in the proteomic discrimination of breast tumors and achieves an in-depth characterization of the subtype's proteomes. Here, we indicate how the variations in the subtype's proteome can influence the tumor's biological and clinical differences, highlighting the variation in the expression pattern of oncoproteins and tumor suppressor proteins between subtypes. Also, through our machine-learning approach, we propose multi-protein panels with the potential to discriminate the breast cancer subtypes. Our panels achieved high classification performance in our cohort and in the independent validation cohort, demonstrating their potential to improve the current tumor discrimination system as complements to the classical immunohistochemical classification.

Major regulators of the multi-step metastatic process are potential therapeutic targets for breast cancer management.

Metastasis is a multi-step process that leads to the dissemination of tumor cells to new sites and, consequently, to multi-organ neoplasia. Although most lethal breast cancer cases are related to metastasis occurrence, little is known about the dysregulation of each step, and clinicians still lack reliable therapeutic targets for metastasis impairment. To fill these gaps, we constructed and analyzed gene regulatory networks for each metastasis step (cell adhesion loss, epithelial-to-mesenchymal transition, and angiogenesis). Through topological analysis, we identified E2F1, EGR1, EZH2, JUN, TP63, and miR-200c-3p as general hub-regulators, FLI1 for cell-adhesion loss specifically, and TRIM28, TCF3, and miR-429 for angiogenesis. Applying the FANMOD algorithm, we identified 60 coherent feed-forward loops regulating metastasis-related genes associated with distant metastasis-free survival prediction. miR-139-5p, miR-200c-3p, miR-454-3p, and miR-1301-3p, among others, were the FFL's mediators. The expression of the regulators and mediators was observed to impact overall survival and to go along with metastasis occurrence. Lastly, we selected 12 key regulators and observed that they are potential therapeutic targets for canonical and candidate antineoplastics and immunomodulatory drugs, like trastuzumab, goserelin, and calcitriol. Our results highlight the relevance of miRNAs in mediating feed-forward loops and regulating the expression of metastasis-related genes. Altogether, our results contribute to understanding the multi-step metastasis complexity and identifying novel therapeutic targets and drugs for breast cancer management.

Comprehensive analysis of the large and small ribosomal proteins in breast cancer: Insights on proteomic and transcriptomic expression patterns, regulation, mutational landscape, and prognostic significance.

Several evidence has demonstrated the involvement of the ribosomal proteins (RPs) in many malignancies, however, the function and clinical relevance of the RPs in breast cancer remains unclear. The present study aims to contribute to the understanding of the role of the RPs in breast tumorigenesis and its clinical implications in the field of biomarker discovery and outcome prediction. We investigated the proteomic and transcriptomic expression of the RPs in non-tumor and tumor tissues of different breast cancer subtypes, and integrated bioinformatics approaches and online databases to comprehensively evaluate the potential functions, regulatory networks, mutational landscape, and prognostic values of the ribosomal proteins in breast cancer. Our results show that 33 RPs have deregulated expression in breast cancer and its subtypes and that 26 RPs have potential as prognostic markers in a subtype-dependent way, with mutations in RP genes being frequent in breast tumors and related to overall survival and relapse-free status. Our RP gene regulatory network indicates the transcription factors MYC, ETS1, and SPI1, and the miRNAs has-let-7c-5p, has-mir-20b-5p, and has-mir-4668-3p as regulators of the RPs expression in breast cancer. The RPs were associated with several clinicopathological parameters of breast cancer and predicted to be involved in ribosomal-independent mechanisms such as regulation of the SLITS-ROBO pathway. This study comprehensively investigated the ribosomal proteins in breast cancer, suggesting that the RPs have clinical potential as biomarkers of diagnostic and prognostic, also providing an in-depth view of the RPs significance in breast cancer.